Purchase this article with an account.
Marjena Popovic, Dmitriy Lisitsyn, Pietro Berkes, Máté Lengyel, József Fiser; Uncertainty representation of low-level visual attributes. Journal of Vision 2011;11(11):807. https://doi.org/10.1167/11.11.807.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
There is increasing behavioral evidence that humans represent uncertainty about sensory stimuli in a way that it is suitable for decision making and learning in a statistically optimal manner. Do such representations of uncertainty exist for low-level visual stimuli, and furthermore, are they probabilistic in nature? We tested whether subjective assessment of the orientation uncertainty of a stimulus consisting of a fixed number of Gabor wavelets of different orientations reflects the true distribution of orientation uncertainty of the stimulus. Textured gray-scale stimuli were created by superimposing Gabor wavelets of three spatial frequency bands with their orientation randomly sampled from a bimodal Gaussian distribution. After 2 seconds of stimulus presentation, two oriented lines were displayed and subjects were asked to indicate the overall orientation of the stimulus by choosing one of the lines, or to opt not to respond if they were uncertain about the orientation. The orientation of the two lines matched the mean orientation of the stimulus orientation distribution and one of the modes. On average, subjects strongly preferred the mode (65%) over the mean (20%) and only rarely chose not to respond (15%) when the distribution of the orientations had two prominent modes. Increasing the variance of each mode led to a gradual reversal of ratios between the “mode” and “uncertain” responses. When the increase of variances changed the shape of the distribution to unimodal, subjects chose the mode 25%, the mean 15%, and the “uncertain” option 60% of the time. Results suggest that uncertainty associated with low level visual stimuli is explicitly represented as a probability distribution at a level of precision that goes beyond that of a simple parametric representation.
This PDF is available to Subscribers Only